In the financial services landscape, where payments, transfers, trades, and numerous transactions take place every day, data sits at the financial system’s core and enables its operation. And where there is data, there is potential room to leverage models that learn from it and can help to optimize the operation. Such models within the machine learning realm have become critical tools for tackling tasks (to name a few) such as fraud detection, customer segmentation, sentiment analysis, and risk assessment. Creating these models has become easier in recent years. However, the same can’t be said about deploying and maintaining these models in production environments. This is where MLOps (Machine Learning Operations) comes into play, offering a set of practices and, hopefully, standards that combine machine learning, DevSecOps, and data engineering to develop, deploy, and maintain ML models more reliably and efficiently.
